Abstract

Computational simulations provide detailed hemodynamics and physiological data that can assist in clinical decision-making. However, accurate cardiovascular simulations require complete 3D models constructed from image data. Though edge localization is a key aspect in pinpointing vessel walls in many segmentation tools, the edge detection algorithms widely utilized by the medical imaging community have remained static. In this paper, we propose a novel approach to medical image edge detection by adopting the powerful structured forest detector and extending its application to the medical imaging domain. First, we specify an effective set of medical imaging driven features. Second, we directly incorporate an adaptive prior to create a robust three-dimensional edge classifier. Last, we boost our accuracy through an intelligent sampling scheme that only samples areas of importance to edge fidelity. Through experimentation, we demonstrate that the proposed method outperforms widely used edge detectors and probabilistic boosting tree edge classifiers and is robust to error in a prori information.